Businesses often employ a salesforce to make sales to customers via sales calls. During a typical sales call, a salesperson may want to present information and data to the customer, such as inventory, prices, interest rates, prior sales figures, competitor information, and the like, in order to provide the customer with information in an attempt to make a sale. Such sales calls may often be short in duration, possibly lasting a minute or two. It can be crucial for salespeople to have quick access to relevant data, therefore, to present to a customer during such a call.
Automated chatbots can sometimes be used to retrieve this information; however, conventional chatbots are generally built to either provide answers to very specific question set (i.e., predetermined questions) or to provide superficial answers to a wide variety of questions. A conventional automated chatbot may be used to provide a user with their account balance, for example, or the temperature in a remote city. Consequently, any query given to the chatbot outside of the very specific question set it is programmed to respond to will be met with an error.
Teaching the chatbot to respond to more queries is a time-consuming and arduous process. The chatbot must be trained with labeled training data, and large amounts of labeled data are required to produce an accurate chatbot. If a query is given which was not in the training data, the chatbot will respond incorrectly or not at all. Conventional training methods therefore require intensive labeling and training processes to catch as many potential cases and queries as possible in a data scraping process. This process requires manual labeling and is a large consumer of manpower. Adaptable systems to respond to a broad variety of queries that can improve over time without the need for pre-labeled data are desirable.
Accordingly, there is a need for improved systems for training a chatbot, or other natural language system, to respond to a broad variety of queries and adapt to new queries not yet labeled in training data. Embodiments of the present disclosure are directed to this and other considerations.